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Browsing Electronics Science and Technology - Publications by Subject "ABC Algorithm"
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    Artificial bee colony algorithm for probabilistic target Q-coverage in wireless sensor networks
    ( 2013-12-01) Mini, S. ; Udgata, Siba K. ; Sabat, Samrat L.
    The lifetime of a wireless sensor network is dependent on the type of sensor deployment. If the application permits deterministic deployment of nodes and if the sensor nodes are limited, quality of sensing and energy conservation can be enhanced by restricting the sensing range requirement. This paper addresses deterministic deployment of nodes for probabilistic target Q-coverage. A probabilistic coverage model considers the effect of distance and medium on the sensing ability of a node. We use Artificial Bee Colony (ABC) algorithm to compute the optimal deployment of sensor nodes such that the required sensing range is minimum for probabilistic target Q-coverage. © 2013 Springer International Publishing.
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    Artificial bee colony based sensor deployment algorithm for target coverage problem in 3-D terrain
    ( 2011-02-21) Mini, S. ; Udgata, Siba K. ; Sabat, Samrat L.
    In this paper we address sensor deployment problem to achieve different types of target coverage, viz; simple coverage, k-coverage and Q-coverage. Energy which is an important and scarce resource is not being optimally used if sensor nodes are randomly deployed in a region. This energy wastage can significantly be reduced if the deployment positions can be optimally computed. It is important to provide required coverage by keeping the required sensing range at minimum which will require less energy for sensing. We find out the optimal deployment positions in a 3-D terrain using Artificial Bee Colony (ABC) algorithm, which is based on swarm intelligence, and also compare the sensing range requirement for simple, k and Q-coverage problems. Experimental results reveal that for dense networks, the required sensing range does not increase in same proportion for increased value of k and increased value of average number of sensor nodes in Q for k-Coverage and Q-Coverage problems respectively. Sensitivity analysis is done to study the change in the required sensing range if the sensor nodes cannot be deployed exactly in the optimal positions. The analysis reveals that there is no significant change in the sensing range if the sensor nodes are deployed in near optimal positions. © 2011 Springer-Verlag.
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    Sensor deployment for probabilistic target k-coverage using artificial bee colony algorithm
    ( 2011-12-01) Mini, S. ; Udgata, Siba K. ; Sabat, Samrat L.
    A higher level of coverage is required for many sensitive applications. Though initial work on target coverage problems in wireless sensor networks used binary sensing model, a more realistic sensing model, the probabilistic sensing model has been used later. This work considers probabilistic k-coverage; where the required level of coverage has to be satisfied with k sensors and each target should also be monitored with a specific probability. We compute the optimal deployment location of sensor nodes, such that the probabilistic coverage as well as the k-coverage requirement is satisfied with the required sensing range being optimal. Preliminary results of using artificial bee colony algorithm to solve deployment problem for probabilistic target k-coverage is reported in this paper. © 2011 Springer-Verlag.
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    Sensor deployment in 3-D terrain using artificial bee colony algorithm
    ( 2010-12-01) Mini, S. ; Udgata, Siba K. ; Sabat, Samrat L.
    The ability to determine the optimal deployment location of sensor nodes in a region to satisfy coverage requirement is a key component of establishing an efficient network. Random deployment of sensor nodes fails to be optimal when nodes are deployed where no targets need to be covered, resulting in wastage of energy. The objective of this paper is to place the given number of sensor nodes such that all targets are covered and the required sensing range is minimum. We model the sensor deployment problem as a clustering problem and the optimal locations for sensor deployment are obtained using Artificial Bee Colony (ABC) algorithm. We analyze how the sensing range varies with the number of sensor nodes and also carry out sensitivity analysis test to find the variation in sensing range if the sensor nodes are deployed in a near optimal position. © 2010 Springer-Verlag.